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Bayesian modelling and quantification of Raman spectroscopy

Journal Article


Abstract


  • Raman spectroscopy can be used to identify molecules such as DNA by the

    characteristic scattering of light from a laser. It is sensitive at very low

    concentrations and can accurately quantify the amount of a given molecule in a

    sample. The presence of a large, nonuniform background presents a major

    challenge to analysis of these spectra. To overcome this challenge, we

    introduce a sequential Monte Carlo (SMC) algorithm to separate each observed

    spectrum into a series of peaks plus a smoothly-varying baseline, corrupted by

    additive white noise. The peaks are modelled as Lorentzian, Gaussian, or

    pseudo-Voigt functions, while the baseline is estimated using a penalised cubic

    spline. This latent continuous representation accounts for differences in

    resolution between measurements. The posterior distribution can be

    incrementally updated as more data becomes available, resulting in a scalable

    algorithm that is robust to local maxima. By incorporating this representation

    in a Bayesian hierarchical regression model, we can quantify the relationship

    between molecular concentration and peak intensity, thereby providing an

    improved estimate of the limit of detection, which is of major importance to

    analytical chemistry.

Publication Date


  • 2016

Citation


  • Moores, M., Gracie, K., Carson, J., Faulds, K., Graham, D., & Girolami, M. (2016). Bayesian modelling and quantification of Raman spectroscopy. Retrieved from http://arxiv.org/abs/1604.07299v2

Web Of Science Accession Number


Abstract


  • Raman spectroscopy can be used to identify molecules such as DNA by the

    characteristic scattering of light from a laser. It is sensitive at very low

    concentrations and can accurately quantify the amount of a given molecule in a

    sample. The presence of a large, nonuniform background presents a major

    challenge to analysis of these spectra. To overcome this challenge, we

    introduce a sequential Monte Carlo (SMC) algorithm to separate each observed

    spectrum into a series of peaks plus a smoothly-varying baseline, corrupted by

    additive white noise. The peaks are modelled as Lorentzian, Gaussian, or

    pseudo-Voigt functions, while the baseline is estimated using a penalised cubic

    spline. This latent continuous representation accounts for differences in

    resolution between measurements. The posterior distribution can be

    incrementally updated as more data becomes available, resulting in a scalable

    algorithm that is robust to local maxima. By incorporating this representation

    in a Bayesian hierarchical regression model, we can quantify the relationship

    between molecular concentration and peak intensity, thereby providing an

    improved estimate of the limit of detection, which is of major importance to

    analytical chemistry.

Publication Date


  • 2016

Citation


  • Moores, M., Gracie, K., Carson, J., Faulds, K., Graham, D., & Girolami, M. (2016). Bayesian modelling and quantification of Raman spectroscopy. Retrieved from http://arxiv.org/abs/1604.07299v2

Web Of Science Accession Number